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Prikvačeni tweet
Some preliminary results (10-layer modified ConvNet, followed by a ReLU + softmax): not that I can see discernible rotation/scaling in the feature maps. Next, should try to suppress the "second-order derivatives" in the kernels.pic.twitter.com/RGOOkPThwg
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TIL what “groups” (and “cardinality”) in Conv2d are, which would have simplified my code. Hate that they use well-established math terms when it simply means the matrix (whose entries are 3x3) is block diagonal. Block has come to mean something else in ConvNet architecture.
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Note to self: Not following Senate impeachment trial, but was so moved by
@RepAdamSchiff closing remark on Day2, that every1 knows we can’t count on Trump to choose country over personal gain. Thought: what if jury vote were anonymous? It’s the only way America can redeem itself.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
The NYT report gave lots of actual details. Impulsive and defensive he was/is (classic Trump), but not for distracting impeachment it appears. Still, a total lack of considering the consequences which are being played out.https://www.nytimes.com/2020/01/04/us/politics/trump-suleimani.html …
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When will we see Republicans blame Pelosi and Schiff for destabilizing the Middle East? They pushed for the impeachment, and Trump in his natural reflex did the only thing he knew in re-election year.
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To check if one layer is "close" to another (for regularization, say), we can't just direct-compare the 16×16 matrices, for they could be very close up to permuting the rows/columns. We'd need to compare all the coefficients of the characteristic polynomial, of which det is one.
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So, to do Gaussian elimination (or take det) on that, we'd use convolution product (which thankfully is commutative). E.g., 3×3 convolve 3×3 is a 5×5 kernel. To get an idea what the Conv layer is doing, we may take the 16×16 determinant, & visualize the resulting large kernel
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As a classic example, the wave equation u_tt=Δu can be turned into a system of two equations (to turn back, we can do a sort of Gaussian elimination). A typical CNN may have 16 or more channels, so that'd be a 16×16 matrix of 3×3 kernels (differential operators)pic.twitter.com/S1efUAHCUp
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Ok, a dirty trick to get only first-order operator is to symmetrize the 3×3 kernel (symmetric wrt the center). Now I see the multiple channels in CNN as a *system* of PDEs, or a matrix-valued differential operator (or in my mind, a differential-operator-valued matrix). Update
pic.twitter.com/HXygY4Xr1y
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My prediction for the coming year: No idea what will come out of the impeachment process, or who is going to win in November. All I know is that it will get uglier (and we will miss the innocent days of 2016). Democracy is dead, again. This time there is no Sparta.https://twitter.com/liuyao12/status/1190076704137601024 …
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Any help with testing would be greatly appreciated :) (Still no suppression on "second-order" operators, and no "energy" regularization. This is the "minimalist" version, so it can be easily added to any existing ResNet.)
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FWIW, here's implementation in PyTorch for DL newbies & researchers alike, based on the classic ResNet. It appears that ResNet may have already reached its capacity on CIFAR10, at 95%, but the same "twist" can be easily applied to any ResNet-based model.https://github.com/liuyao12/pytorch-cifar/blob/master/cifar10_with_PDE.ipynb …
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enjoyable read as alwayshttps://twitter.com/JimPropp/status/1192841662298566656 …
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I put my Colab notebook out, and had a chance to pitch it. If to rewrite on Twitter it’d be several tweets long, so
(Apologies for my frustration at self-promotion.)pic.twitter.com/syhcwR1bJs
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Finally did some testing on MNIST... more works needed to make it workhttps://colab.research.google.com/gist/liuyao12/ee803cf1554082f8ab9f89491b3263e5/mnist_with_pde.ipynb …
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It’s only fitting to do the same to Xi Jinping, but I can’t separate what is Xi asserting himself and what is people around him currying favors (as eunuchs did in late Han and late Ming). That’s the biggest mystery.
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With all the leaks and what Trump spouts out daily, most of the craziness “made sense”. (Trump tower meeting? You bet.) Thus, my top three are: 1) How did Tillerson get his job as sec of state? 2) Jeffrey Epstein’s death. 3) Why did Trump withdraw from supporting the Kurds?
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As fun as it is to ponder current US politics against the long history of imperial China (Trump = the idiotic son to ascend the throne), here’s a new one: what’s the Trump equivalent of the three 疑案 of late Ming (palace intrigues)?
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Conspiracy theory of the day: Putin and/or Erdorgan are foreseeing the end of Trump and decide to cash in whatever they have on him, which they’d expose anyway to further plunge US into humiliation and constitutional chaos.
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Just found that it was tweeted before, but no mention of Bai Juyi at the time.https://twitter.com/babelstone/status/1176198055965732864?s=21 …
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